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You searched for +publisher:"University of Edinburgh" +contributor:("Armstrong, Douglas"). One record found.

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University of Edinburgh

1. Alevizos, Ilias. Implementation of neural plasticity mechanisms on reconfigurable hardware for robot learning.

Degree: 2011, University of Edinburgh

It is often assumed that insects are “primitive” animals, without the ability to exhibit complex learning behaviour. Fortunately, their tiny brains quite often surprise us with their performance. This thesis investigates the plasticity mechanisms of the insect brain through the research method of neurorobotics, i.e., the development of a physical agent, equipped with a silicon brain. In order to implement such a brain, we have chosen to model it directly onto hardware. Not only does this allow us to take advantage of the inherent hardware parallelism, but the robot can also behave in a completely autonomous mode, without having to communicate with the software simulator of a remote machine. FPGAs offer both the option for such a lowlevel design approach and the flexibility required in computational studies of biological neural networks. With the use of VHDL (a hardware description language), we develop a simulator for neural networks, designed as a series of computational modules, running in parallel and solving the differential equations which describe neural processes. It has the ability to simulate networks with spiking neurons that follow a phenomenological model, proposed by Izhikevich, which requires only 13 operations per 1 ms of simulation. The synaptic plasticity mechanism can be either that of spike timing-dependent plasticity (STDP) or a modified version of STDP which is also affected by neuromodulators. There are no constraints, as far as the connectivity pattern is concerned. The hardware simulator is then added as a peripheral to an embedded system so that it can be more easily controlled through software and connected to a robot. We show that this hardware system is able to model networks with hundreds of neurons and with a speed performance that is better than real-time. With some slight modifications, it could also scale up to thousands of neurons, starting to approach the size of the insect brain. Subsequently, we use the simulator in order to model a neural network with an architecture inspired by the insect brain, representing the connectivity of the antennal lobe, the mushroom body and the lateral horn, structures which are part of the insect’s olfactory pathway. Our silicon brain is then attached to a robot and its limits and capabilities are tested in a series of experiments. The experiments involve tasks of associative learning inside an arena which is based on a T-maze set-up usually employed in behavioural experiments with flies. The robot is trained to associate different stimuli (or combinations of stimuli) with a punishment, as indicated by the presence of a light source. We observe that the robot can solve most of the tasks, including elemental learning, discrimination learning, biconditional discrimination and negative patterning but fails to solve the problem of positive patterning. It is concluded that the architecture of the insect’s olfactory pathway has the computational efficiency to solve even non-elemental learning tasks. However, this pattern of results does not precisely… Advisors/Committee Members: Webb, Barbara, Armstrong, Douglas.

Subjects/Keywords: insect brain; memory; learning; STDP; FPGA; biorobotics

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APA (6th Edition):

Alevizos, I. (2011). Implementation of neural plasticity mechanisms on reconfigurable hardware for robot learning. (Thesis). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/5778

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Alevizos, Ilias. “Implementation of neural plasticity mechanisms on reconfigurable hardware for robot learning.” 2011. Thesis, University of Edinburgh. Accessed December 07, 2019. http://hdl.handle.net/1842/5778.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Alevizos, Ilias. “Implementation of neural plasticity mechanisms on reconfigurable hardware for robot learning.” 2011. Web. 07 Dec 2019.

Vancouver:

Alevizos I. Implementation of neural plasticity mechanisms on reconfigurable hardware for robot learning. [Internet] [Thesis]. University of Edinburgh; 2011. [cited 2019 Dec 07]. Available from: http://hdl.handle.net/1842/5778.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Alevizos I. Implementation of neural plasticity mechanisms on reconfigurable hardware for robot learning. [Thesis]. University of Edinburgh; 2011. Available from: http://hdl.handle.net/1842/5778

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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